VijayKumar_2012關(guān)于四軸飛行器的演講稿(中英文對(duì)照).doc

早上好我今天想談?wù)勛灾黠w行沙灘球其實(shí)，是小型飛行器，像這一個(gè)我想和大家談?wù)勗O(shè)計(jì)這些飛行器時(shí)的挑戰(zhàn)和使用這些飛行器能給我們帶來(lái)的很多用處這些飛行器源于無(wú)人駕駛的飛行器但是那些都體積很大通常上萬(wàn)磅重毫無(wú)靈活型可言它們也不是真的自主飛行的事實(shí)上，很多這些飛行器都是受飛行團(tuán)隊(duì)控制的包括好幾個(gè)飛行員感應(yīng)雷達(dá)操作員和團(tuán)隊(duì)協(xié)調(diào)員我們想設(shè)計(jì)的飛行器是這樣的這里有兩張照片是你能夠在超市里買(mǎi)到的那種小飛行器小型直升機(jī)，四個(gè)螺旋槳不超過(guò)一米長(zhǎng)只不過(guò)幾磅重我們把它們稍微改造一下，加上感應(yīng)器和處理器，它們就可以在室內(nèi)飛用不著導(dǎo)航系統(tǒng)我現(xiàn)在拿著的這個(gè)飛行器是其中之一是兩個(gè)學(xué)生做出來(lái)的艾利克斯和丹尼爾這個(gè)僅僅比零點(diǎn)一磅稍微重一點(diǎn)只需要大約十五瓦的電源你能看到它的直徑大約只有八個(gè)英寸讓我給你們快速解釋一下這些飛行器是怎么工作的它有四個(gè)螺旋槳當(dāng)四個(gè)螺旋槳轉(zhuǎn)速相同這個(gè)飛行器就浮在空中當(dāng)所有螺旋槳的速度提升時(shí)這個(gè)飛行器就加速升高當(dāng)然了，如果飛行器已經(jīng)是傾斜的向著地平線側(cè)過(guò)來(lái)就會(huì)向這個(gè)方向加速怎么能讓它側(cè)過(guò)來(lái)呢，有兩個(gè)途徑從這張照片你能看到四號(hào)螺旋槳旋轉(zhuǎn)加速同時(shí)二號(hào)螺旋槳轉(zhuǎn)速變慢這時(shí)飛行器就能向一邊倒反之亦然當(dāng)三號(hào)螺旋槳加速一號(hào)減速時(shí)飛行器就向前倒最后如果任意兩端的螺旋槳的轉(zhuǎn)速大于另兩端的螺旋槳的轉(zhuǎn)速飛行器就能原地旋轉(zhuǎn)所以裝在飛行器上的處理器基本上能判斷需要執(zhí)行哪些動(dòng)作然后把它們組合起來(lái)決定給螺旋槳下什么指令一秒鐘六百次簡(jiǎn)單地說(shuō)這些飛行器就是這么工作的這個(gè)設(shè)計(jì)的一個(gè)好處就是小巧這些飛行器很靈活這里的R是飛行器的長(zhǎng)度其實(shí)是半徑當(dāng)半徑變小時(shí)很多物理參數(shù)都會(huì)變最重要的一個(gè)參數(shù)是慣性， 也就是對(duì)于運(yùn)動(dòng)的阻力結(jié)果是慣性決定角速度它是半徑的五次方函數(shù)當(dāng)半徑變得越來(lái)越小時(shí)慣性越來(lái)越快地減小另一個(gè)結(jié)果是角速度的加速度也就是這里的希臘字母alpha等于一除以半徑也就是半徑的倒數(shù)當(dāng)半徑越小時(shí)飛行器能轉(zhuǎn)彎越快這個(gè)視頻清楚地顯示大家看右下角的飛行器正在做一個(gè)三百六十度翻轉(zhuǎn)只需要不到半秒連續(xù)翻轉(zhuǎn)，稍微時(shí)間長(zhǎng)一點(diǎn)這里飛行器上用的處理器能夠從飛行器上的加速度計(jì)和陀螺儀得到反饋信息然后算出，就像我剛才講的一秒鐘六百個(gè)指令來(lái)穩(wěn)定控制這個(gè)飛行器在左邊你能看到丹尼爾把飛行器拋到空中你能看到飛行器的控制有多快不管你怎么扔飛行器都能恢復(fù)平衡飛回來(lái)為什么我們要設(shè)計(jì)這種飛行器呢？因?yàn)檫@樣的飛行器有很多用處你能把它們放進(jìn)像這樣的大樓里作為報(bào)警器去尋找入侵者尋找生化泄漏或者煤氣泄漏你還能用它們建摩天大樓呢這里是飛行器在搬梁運(yùn)柱架構(gòu)一個(gè)立方體的建筑這里我想和大家介紹一下這些機(jī)器人能被用來(lái)運(yùn)貨當(dāng)然一個(gè)問(wèn)題是這些小飛行器擔(dān)不了多少重量你可能需要很多飛行器來(lái)搬運(yùn)重物我們新做了個(gè)實(shí)驗(yàn)其實(shí)不那么新了在日本仙臺(tái)，地震后不久我們能把這些飛行器送進(jìn)倒塌的樓房或者核反應(yīng)堆大樓來(lái)探測(cè)放射性強(qiáng)度一個(gè)根本的問(wèn)題是當(dāng)這些飛行器需要自控飛行，它們自己得弄明白怎么從一個(gè)地點(diǎn)到另一個(gè)地點(diǎn)這就變得有點(diǎn)難度了因?yàn)檫@些飛行器的動(dòng)力學(xué)是很復(fù)雜的事實(shí)上它們總在對(duì)付十二維的空間這里我們用了一點(diǎn)小技巧我們拿這個(gè)十二位的空間把它們轉(zhuǎn)換成平的四維空間這個(gè)四維空間包括了橫軸，縱軸和豎軸，還有旋轉(zhuǎn)軸這些飛行器只需要計(jì)劃一件事，我們管它叫最小化加加加速度軌道提醒大家一點(diǎn)點(diǎn)物理學(xué)這里我們有位置向量，導(dǎo)數(shù)，速度和加速度還有加加速度還有加加加速度這個(gè)飛行器把加加加速度最小化基本上它的工作是創(chuàng)造一個(gè)光滑優(yōu)雅的運(yùn)動(dòng)曲線這樣來(lái)繞開(kāi)障礙物所以這個(gè)四維平面中，這個(gè)飛行器使用最小化加加加速度軌道， 然后轉(zhuǎn)換回到復(fù)雜的十二維空間飛行器必須這樣做來(lái)獲得控制和執(zhí)行動(dòng)作讓我給大家看幾個(gè)例子這些最小化加加加速度軌道是什么樣的這是第一個(gè)視頻這個(gè)飛行器從一個(gè)地點(diǎn)飛到另一個(gè)地點(diǎn)中間經(jīng)停一下顯然這個(gè)飛行器能飛出一個(gè)曲線軌道還有這樣的打圈的軌道這里飛行器對(duì)抗兩倍的重力它們上方還有一個(gè)動(dòng)感監(jiān)控?cái)z像機(jī)，每秒一百幅畫(huà)面來(lái)告訴這些飛行器它們的位置也能告訴這些飛行器障礙物在哪里障礙物移動(dòng)都不要緊當(dāng)?shù)つ釥柊烟兹θ拥娇罩酗w行器就開(kāi)始計(jì)算套圈的位置試圖預(yù)測(cè)怎么才能最有效地鉆過(guò)去作為一個(gè)科研人員我們總在試圖鉆出重重圈套，拿到更多經(jīng)費(fèi)甚至訓(xùn)練了我們的飛行器也來(lái)做這個(gè)（掌聲）另一個(gè)飛行器能做的事情是當(dāng)我們預(yù)先編入一些軌跡或者它自己學(xué)著走過(guò)的，它能夠記住這里大家能看到飛行器能夠（在預(yù)設(shè)軌跡上）加上一個(gè)動(dòng)作積聚動(dòng)量改變它的定向，再回到預(yù)設(shè)軌跡上來(lái)它必須這樣做因?yàn)檫@個(gè)窗上的縫隙只比它的寬度大一點(diǎn)點(diǎn)所以就像是一個(gè)跳水運(yùn)動(dòng)員從跳板上起跳，聚集動(dòng)量，做個(gè)旋轉(zhuǎn)，兩圈半然后優(yōu)雅地回到平衡這個(gè)飛行器是自主這樣做的它知道怎么把小段的軌跡組合起來(lái)來(lái)做這些高難度的技巧現(xiàn)在我想換個(gè)話題談?wù)勥@些小型飛行器的不足之處，就是體積小我已經(jīng)提過(guò)我們需要使用很多飛行器來(lái)克服體積小的不便一個(gè)難點(diǎn)是怎么使得這些飛行器集體飛行？我們?cè)诖笞匀恢袑ふ掖鸢肝蚁虢o大家看一個(gè)視頻是關(guān)于Aphaenogaster沙漠蟻的在史狄文普熱特教授的實(shí)驗(yàn)室里，這些螞蟻一起搬運(yùn)重物這是一個(gè)無(wú)花果事實(shí)上無(wú)論什么東西，只要蘸上無(wú)花果汁這些螞蟻都會(huì)把它們帶回巢去這些螞蟻沒(méi)有任何中央調(diào)控它們是靠感應(yīng)鄰近的螞蟻它們也沒(méi)有明確的交流但是因?yàn)樗鼈兡軌蚋袘?yīng)鄰近的螞蟻也能感應(yīng)抬著的重物整群的螞蟻有默契這樣的協(xié)調(diào)正是飛行器需要的當(dāng)一個(gè)飛行器被其他飛行器環(huán)繞時(shí)讓我們注意 I 和 J 這兩個(gè)當(dāng)它們成群飛行時(shí)我們希望這兩個(gè)飛行器能夠監(jiān)控它們之間的距離我們需要確定這個(gè)距離是在可接受的范圍里的飛行器要檢測(cè)這個(gè)變化在控制指令中計(jì)算進(jìn)去也是每秒一百次這個(gè)控制指令每秒會(huì)被送到馬達(dá)六百次所以這個(gè)程序是分散化執(zhí)行的再有，如果你有很多很多飛行器要完成集體飛行任務(wù)，能足夠快地集中協(xié)調(diào)所有這些信息是幾乎不可能的加上這些飛行器只能依靠局部的信息來(lái)決定做什么動(dòng)作也就是要靠感應(yīng)鄰近的飛行器最后我們希望這些機(jī)器人不知道它們的鄰居是誰(shuí)也就是匿名飛行下一個(gè)我想給大家展示的是這段視頻這二十個(gè)小型飛行器成群飛行它們?cè)诒O(jiān)測(cè)鄰居的位置維持群隊(duì)群隊(duì)的形狀還能變它們可以在一個(gè)平面上飛也可以上中下地飛大家可以看到它們能從上中下的群隊(duì)變成平面的在飛越障礙物的時(shí)候它們能邊飛邊變換隊(duì)形我想強(qiáng)調(diào)，這些飛行器距離都很近比如這個(gè)群隊(duì)，八架飛行器相互距離不過(guò)幾英寸盡管在空氣動(dòng)力學(xué)上這些螺旋槳相互干擾它們還是能夠維持平穩(wěn)飛行（掌聲）現(xiàn)在它們會(huì)成群飛了它們就可以合作抬重物這里展示的是我們能夠把飛行器的能力翻倍，翻三倍，四倍僅僅通過(guò)讓它們和鄰居合作，大家可以看到這樣做的一個(gè)不便之處就是當(dāng)加大數(shù)量時(shí)比如使用很多飛行器來(lái)抬一個(gè)物體你其實(shí)是加大了慣性這樣它們就不夠靈活了，這是一個(gè)代價(jià)但是你可以增加載荷承載量另一個(gè)我想給大家展示的用處是這是在我們實(shí)驗(yàn)室這是研究生昆汀林夕的工作他的算法程序告訴這些飛行器怎么使用桁架結(jié)構(gòu)自動(dòng)建造一個(gè)立方體他的算法程序告訴這些機(jī)器人該用哪一塊什么時(shí)候用，用在哪里從這個(gè)視頻我們可以看到這個(gè)視頻是十倍或者十四倍速度播放的大家可以看到飛行器在搭建很不一樣的構(gòu)架并且，所有的運(yùn)動(dòng)都是自主的昆汀僅僅是給它們一個(gè)藍(lán)圖也就是他想建的設(shè)計(jì)所有這里展示的實(shí)驗(yàn)所有這些演習(xí)都是靠著它們自己的動(dòng)感檢測(cè)攝像機(jī)完成的那么，當(dāng)它們離開(kāi)實(shí)驗(yàn)室來(lái)到真實(shí)世界的時(shí)候，又怎么樣呢？沒(méi)有衛(wèi)星導(dǎo)航會(huì)怎么樣？這個(gè)飛行器其實(shí)裝有一個(gè)攝像機(jī)和一個(gè)激光測(cè)距儀，一個(gè)激光掃描儀它可以使用這些探測(cè)裝置來(lái)描繪周?chē)沫h(huán)境的地圖這個(gè)地圖包括很多細(xì)節(jié)玄關(guān)，窗戶(hù)人，家具還能弄清楚相對(duì)于這些東西它自己在哪里所以這里沒(méi)有整體的協(xié)調(diào)系統(tǒng)這個(gè)協(xié)調(diào)系統(tǒng)是靠飛行器自己來(lái)完成的它自己在哪里，前面有什么還能利用周?chē)h(huán)境為自己找到出路這里我想給大家再看一段視頻這個(gè)算法程序是法蘭克沈和南希麥克教授編的當(dāng)這個(gè)飛行器第一次飛入一個(gè)建筑它是怎么邊飛邊畫(huà)地圖的這個(gè)飛行器弄明白了這些細(xì)節(jié)開(kāi)始畫(huà)地圖弄明白了相對(duì)這些細(xì)節(jié)，自己在哪里，然后自我定位全以每秒一百次的速度發(fā)生這就給我們一個(gè)機(jī)會(huì)來(lái)控制這些算法像我之前講過(guò)的所以這個(gè)機(jī)器人其實(shí)是被法蘭克遙控的但是它自己也可以弄明白怎么飛假設(shè)我想放一個(gè)這樣的飛行器進(jìn)一幢樓我并不知道里面是什么樣的我可以讓它飛進(jìn)去創(chuàng)造一個(gè)地圖然后飛回來(lái)告訴我里面是什么樣的所以，這個(gè)飛行器不僅僅解決了怎么從一點(diǎn)到另一點(diǎn)的問(wèn)題還能夠隨時(shí)知道最好的目標(biāo)在哪里基本上，它知道該去搜索哪里因?yàn)槟抢锏男畔⑹亲睢拔粗钡倪@就是它怎么填充這個(gè)地圖這里我想展示給大家最后一個(gè)用途當(dāng)然這個(gè)技術(shù)有很多很多用途我是個(gè)教授，我們很關(guān)心教育這樣的飛行器其實(shí)可以改變我們的小學(xué)和中學(xué)教育我們?cè)谀霞又蓦x洛杉磯很近所以我不得不放點(diǎn)娛樂(lè)元素進(jìn)去我想給大家看一個(gè)音樂(lè)視頻我想向你們介紹艾利克斯和丹尼爾，他們是導(dǎo)演兼制作（掌聲）在我播放這個(gè)視頻前我想告訴大家這是他們?cè)谶^(guò)去三天做出來(lái)的因?yàn)橹鞒秩丝巳鹚菇o我打了個(gè)電話在這個(gè)視頻中表演的飛行器全是靠自控表演的你能看到九個(gè)機(jī)器人，演奏六種不同樂(lè)器當(dāng)然了，這是為了今年的TED2012特別制作的請(qǐng)欣賞（音樂(lè)）（掌聲）Good morning.Im here today to talkabout autonomous, flying beach balls.No, agile aerial robots like this one.Id like to tell you a little bit about the challenges in building theseand some of the terrific opportunitiesfor applying this technology.So these robotsare related to unmanned aerial vehicles.However, the vehicles you see here are big.They weigh thousands of pounds,are not by any means agile.Theyre not even autonomous.In fact, many of these vehiclesare operated by flight crewsthat can include multiple pilots,operators of sensorsand mission coordinators.What were interested in is developing robots like this -and here are two other pictures -of robots that you can buy off the shelf.So these are helicopters with four rotorsand theyre roughly a meter or so in scaleand weigh several pounds.And so we retrofit these with sensors and processors,and these robots can fly indoorswithout GPS.The robot Im holding in my handis this one,and its been created by two students,Alex and Daniel.So this weighs a little morethan a tenth of a pound.It consumes about 15 watts of power.And as you can see,its about eight inches in diameter.So let me give you just a very quick tutorialon how these robots work.So it has four rotors.If you spin these rotors at the same speed,the robot hovers.If you increase the speed of each of these rotors,then the robot flies up, it accelerates up.Of course, if the robot were tilted,inclined to the horizontal,then it would accelerate in this direction.So to get it to tilt, theres one of two ways of doing it.So in this pictureyou see that rotor four is spinning fasterand rotor two is spinning slower.And when that happenstheres moment that causes this robot to roll.And the other way around,if you increase the speed of rotor threeand decrease the speed of rotor one,then the robot pitches forward.And then finally,if you spin opposite pairs of rotorsfaster than the other pair,then the robot yaws about the vertical axis.So an on-board processoressentially looks at what motions need to be executedand combines these motionsand figures out what commands to send to the motors600 times a second.Thats basically how this thing operates.So one of the advantages of this designis, when you scale things down,the robot naturally becomes agile.So here Ris the characteristic length of the robot.Its actually half the diameter.And there are lots of physical parameters that changeas you reduce R.The one thats the most importantis the inertia or the resistance to motion.So it turns out,the inertia, which governs angular motion,scales as a fifth power of R.So the smaller you make R,the more dramatically the inertia reduces.So as a result, the angular acceleration,denoted by Greek letter alpha here,goes as one over R.Its inversely proportional to R.The smaller you make it the more quickly you can turn.So this should be clear in these videos.At the bottom right you see a robotperforming a 360 degree flipin less than half a second.Multiple flips, a little more time.So here the processes on boardare getting feedback from accelerometersand gyros on boardand calculating, like I said before,commands at 600 times a secondto stabilize this robot.So on the left, you see Daniel throwing this robot up into the air.And it shows you how robust the control is.No matter how you throw it,the robot recovers and comes back to him.So why build robots like this?Well robots like this have many applications.You can send them inside buildings like thisas first responders to look for intruders,maybe look for biochemical leaks,gaseous leaks.You can also use themfor applications like construction.So here are robots carrying beams, columnsand assembling cube-like structures.Ill tell you a little bit more about this.The robots can be used for transporting cargo.So one of the problems with these small robotsis their payload carrying capacity.So you might want to have multiple robotscarry payloads.This is a picture of a recent experiment we did -actually not so recent anymore -in Sendai shortly after the earthquake.So robots like this could be sent into collapsed buildingsto assess the damage after natural disasters,or sent into reactor buildingsto map radiation levels.So one fundamental problemthat the robots have to solve if theyre to be autonomousis essentially figuring outhow to get from point A to point B.So this gets a little challengingbecause the dynamics of this robot are quite complicated.In fact, they live in a 12-dimensional space.So we use a little trick.We take this curved 12-dimensional spaceand transform itinto a flat four-dimensional space.And that four-dimensional spaceconsists of X, Y, Z and then the yaw angle.And so what the robot doesis it plans what we call a minimum snap trajectory.So to remind you of physics,you have position, derivative, velocity,then acceleration,and then comes jerkand then comes snap.So this robot minimizes snap.So what that effectively doesis produces a smooth and graceful motion.And it does that avoiding obstacles.So these minimum snap trajectories in this flat spaceare then transformed backinto this complicated 12-dimensional space,which the robot must dofor control and then execution.So let me show you some examplesof what these minimum snap trajectories look like.And in the first video,youll see the robot going from point A to point Bthrough an intermediate point.So the robot is obviously capableof executing any curve trajectory.So these are circular trajectorieswhere the robot pulls about two Gs.Here you have overhead motion capture cameras on the topthat tell the robot where it is 100 times a second.It also tells the robot where these obstacles are.And the obstacles can be moving.And here youll see Daniel throw this hoop into the air,while the robot is calculating the position of the hoopand trying to figure out how to best go through the hoop.So as an academic,were always trained to be able to jump through hoops to raise funding for our labs,and we get our robots to do that.(Applause)So another thing the robot can dois it remembers pieces of trajectorythat it learns or is pre-programmed.So here you see the robotcombining a motionthat builds up momentumand then changes its orientation and then recovers.So it has to do this because this gap in the windowis only slightly larger than the width of the robot.So just like a diver stands on a springboardand then jumps off it to gain momentum,and then does this pirouette, this two and a half somersault throughand then gracefully recovers,this robot is basically doing that.So it knows how to combine little bits and pieces of trajectoriesto do these fairly difficult tasks.So I want change gears.So one of the disadvantages of these small robots is its size.And I told you earlierthat we may want to employ lots and lots of robotsto overcome the limitations of size.So one difficultyis how do you coordinate lots of these robots?And so here we looked to nature.So I want to show you a clipof Aphaenogaster desert antsin Professor Stephen Pratts lab carrying an object.So this is actually a piece of fig.Actually you take any object coated with fig juiceand the ants will carry them back to the nest.So these ants dont have any central coordinator.They sense their neighbors.Theres no explicit communication.But because they sense the neighborsand because they sense the object,they have implicit coordination across the group.So this is the kind of coordinationwe want our robots to have.So when we have a robotwhich is surrounded by neighbors -and lets look at robot I and robot J -what we want the robots to dois to monitor the separation between themas they fly in formation.And then you want to make surethat this separation is within acceptable levels.So again the robots monitor this errorand calculate the control commands100 times a second,which then translates to the motor commands 600 times a second.So this also has to be donein a decentralized way.Again, if you have lots and lots of robots,its impossible to coordinate all this information centrallyfast enough in order for the robots to accomplish the task.Plus the robots have to base their actionsonly on local information,what they sense from their neighbors.And then finally,we insist that the robots be agnosticto who their neighbors are.So this is what we call anonymity.So what I want to show you nextis a videoof 20 of these little robotsflying in formation.Theyre monitoring their neighbors position.Theyre maintaining formation.The formations can change.They can be planar formations,they can be three-dimensional formations.As you can see here,they collapse from a three-dimensional formation into planar formation.And to fly through obstaclesthey can adapt the formations on the fly.So again, these robots come really close together.As you can see in this figure-eight flight,they come within inches of each other.And despite the aerodynamic interactionsof these propeller blades,theyre able to maintain stable flight.(Applause)So once you know how to fly in formation,you can actually pick up objects cooperatively.So this just showsthat we can double, triple, quadruplethe robot strengthby just getting them to team with neighbors, as you can see here.One of the disadvantages of doing thatis, as you scale things up -so if you have lots of robots carrying the same thing,youre essentially effectively increasing the inertia,and therefore you pay a price; theyre not as agile.But you do gain in terms of payload carrying capacity.Another application I want to show you -again, this is in our lab.This is work done by Quentin Lindsey whos a graduate student.So his algorithm essentially tells these robotshow to autonomously buildcubic structuresfrom truss-like elements.So his algorithm tells the robotwhat part to pick up,when and where to place it.So in this video you see -and its sped up 10, 14 times -you see three different structures being built by these robots.And again, everything is autonomous,and all Quentin has to dois to get them a blueprintof the design that he wants to build.So all these experiments youve seen thus far,all these demonstrations,have been done with the help of motion capture systems.So what happens when you leave your laband you go outside into the real world?And what if theres no GPS?So this robotis actually equipped with a cameraand a laser rangefinder, laser scanner.And it uses these sensorsto build a map of the environment.What that map consists of are features -like doorways, windows,people, furniture -and it then figures out where its position iswith respect to the features.So there is no global coordinate system.The coordinate system is defined based on the robot,where it is and what its looking at.And it navigates with respect to those features.So I want to show you a clipof algorithms developed by Frank Shenand Professor Nathan Michaelthat shows this robot entering a building for the very first timeand creating this map